hKG Ontologizer
Transforms text content and web URLs into structured knowledge graphs using entity extraction and relationship mapping, with support for multiple file formats, real-time visualization, and persistent storage in Neo4j and Qdrant databases.
About
This MCP server transforms text content and web URLs into structured knowledge graphs using AI-powered entity extraction and relationship mapping, built with Python and Gradio for the MCP Hackathon 2025. The implementation supports multiple AI model providers (Ollama, LM Studio, hosted APIs) for flexible deployment, processes large content through intelligent chunking with real-time visualization updates, and integrates with Neo4j for graph storage and Qdrant for vector embeddings using UUIDv8 for unified entity tracking. Features include real-time SVG graph generation with NetworkX and matplotlib, comprehensive file format support (PDF, DOCX, CSV, HTML), and enhanced metadata tracking for knowledge graph lineage, serving researchers analyzing large documents, developers building knowledge management systems, and teams needing automated extraction of structured insights from unstructured content with persistent graph storage capabilities.
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